Analysis of Individual CO2 Carbon Footprint Predictions in the Environment by Comparing XGBoost Classifier and Cat Boost Classifier Algorithms
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Analysis of Individual CO2 Carbon Footprint Predictions in the Environment by Comparing XGBoost Classifier and Cat Boost Classifier AlgorithmsAbstract
Climate change caused by global warming has become a global issue that is of concern to many. One of the main factors is greenhouse gases, which trigger the greenhouse effect in the Earth's atmosphere. Currently, the concentration of carbon dioxide in the atmosphere is estimated to be at the highest level in history. Carbon emissions come from human organizations, activities, products, and activities, which are referred to as carbon footprints. The carbon footprint serves as an indicator of human activities affecting the environment. The issue of carbon emissions will be a trend from year to year, especially in developing and developed countries that have high consumption of motor vehicles. Predicting the carbon footprint of individual CO₂ on the environment by comparing the XGBoost Classifier and Cat Boost Classifier algorithms showed that the XGBoost Classifier was the best performing model with an accuracy value of 0.997, followed by the CatBoost Classifier. This shows that electricity consumption and waste increase the carbon footprint, while renewable energy reduces the carbon footprint, and eco-friendly measures have a small but small impact.
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